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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2022

Description and Genome Characterization of Three Novel Fungal Strains Isolated from Mars 2020 Mission-Associated Spacecraft Assembly Facility Surfaces—Recommendations for Two New Genera and One Species

National Aeronautics and Space Administration’s (NASA) spacecraft assembly facilities are monitored for the presence of any bacteria or fungi that might conceivably survive a transfer to an extraterrestrial environment. Fungi present a broad and diverse range of phenotypic and functional traits to adapt to extreme conditions, hence the detection of fungi and subsequent eradication of them are needed to prevent forward contamination for future NASA missions. During the construction and assembly for the Mars 2020 mission, three fungal strains with unique morphological and phylogenetic properties were isolated from spacecraft assembly facilities. The reconstruction of phylogenetic trees based on several gene loci (ITS, LSU, SSU, RPB, TUB, TEF1) using multi-locus sequence typing (MLST) and whole genome sequencing (WGS) analyses supported the hypothesis that these were novel species. Here we report the genus or species-level classification of these three novel strains via a polyphasic approach using phylogenetic analysis, colony and cell morphology, and comparative analysis of WGS. The strain FJI-L9-BK-P1 isolated from the Jet Propulsion Laboratory Spacecraft Assembly Facility (JPL-SAF) exhibited a putative phylogenetic relationship with the strain Aaosphaeria arxii CBS175.79 but showed distinct morphology and microscopic features. Another JPL-SAF strain, FJII-L3-CM-DR1, was phylogenetically distinct from members of the family Trichomeriaceae and exhibited morphologically different features from the genera Lithohypha and Strelitziana. The strain FKI-L1-BK-DR1 isolated from the Kennedy Space Center facility was identified as a member of Dothideomycetes incertae sedis and is closely related to the family Kirschsteiniotheliaceae according to a phylogenetic analysis. The polyphasic taxonomic approach supported the recommendation for establishing two novel genera and one novel species. The names Aaosphaeria pasadenensis (FJI-L9-BK-P1 = NRRL 64424 = DSM 114621), Pasadenomyces melaninifex (FJII-L3-CM-DR1 = NRRL 64433 = DSM 114623), and Floridaphiala radiotolerans (FKI-L1-BK-DR1 = NRRL 64434 = DSM 114624) are proposed as type species. Furthermore, resistance to ultraviolet-C and presence of specific biosynthetic gene cluster(s) coding for metabolically active compounds are unique to these strains.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology

Parkinson’s disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician’s initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson’s medication changes—clinically assessed by the MDS-Unified Parkinson’s Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose—with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels

Abstract. The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased CO2 concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we provide a methodology for properly diagnosing the impact of changing obscuration on cloud feedbacks and adjustments and quantify these effects across climate models. Averaged globally and across global climate models, properly accounting for obscuration leads to weaker positive feedbacks from lower-level clouds and stronger positive feedbacks from upper-level clouds while simultaneously removing a mostly artificial anti-correlation between them. Given that the methodology for diagnosing cloud feedbacks and adjustments using cloud radiative kernels has evolved over several papers, and obscuration effects have only occasionally been considered in recent papers, this paper serves to establish recommended best practices and to provide a corresponding code base for community use.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Aligning NASA Earth Science Data Stewardship with FAIR Principles: Outcomes, Recommendations, and Future Directions

The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—offer a widely accepted framework for improving the sharing and reuse of digital scientific data by both human and machine users. Following these principles is critical for effective scientific data stewardship, broader scientific collaboration, and compliance with federal and agency data policies. This paper, based on the work of NASA’s Open, Free, and FAIR Working Group (O’FAIR WG) under the Earth Science Data Systems Program, presents an overview of how FAIR is being applied within NASA’s Earth science data landscape. It highlights ongoing progress and challenges, identifies FAIR-enabling resources, and offers recommendations and strategic actions to enhance the FAIRness of NASA-funded open and free Earth science data products. The FAIR-enabling resources identified underscore the vital role of NASA's existing enterprise processes, standards, tools, and infrastructures in supporting FAIR implementation. Our findings show strong performance in making NASA Earth science data more findable and accessible. However, further work is needed—especially in enhancing interoperability, so that different systems and tools can better understand and exchange data. This is especially important for enabling machine-driven discovery and analysis. We emphasize the importance of a balanced strategy that combines a centralized, top-down approach—focused on building enterprise-level capabilities and processes—with a decentralized, bottom-up approach driven by discipline-specific needs and community practices. We advocate for coordinated efforts to enhance (meta)data interoperability to facilitate seamless data and information sharing and exchange of Earth science data both within NASA and across other agencies managing Earth science data.

Data Product↗

DOE OSTI2026

PSTN-057: Survey Cadence Optimization Committee's Survey Start Recommendations

A recommendation for adjustments to the initial LSST survey strategy is delivered to the Observatory Director. These adjustments respond to improved knowledge and understanding of the system performance and completeness of science verification activities at the start of LSST.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1968

Geologic characteristics of the nine lunar landing mission sites recommended by the group for lunar exploration planning

Geologic characteristics of nine lunar landing mission sites recommended by Group for Lunar Exploration Planning

El-Baz, F.↗

NASA NTRS1969

An analysis of low cost management approaches for the development of the Earth Observatory Satellite (EOS) is presented. The factors of the program which tend to increase costs are identified. The NASA/Industry interface is stressed to show how the interface can be improved to produce reduced program costs. Techniques and examples of cost reduction which can be applied to the EOS program are tabulated. Specific recommendations for actions to be taken to reduce costs in prescribed areas are submitted.

Source record↗

NASA NTRS1974

Earth Observatory Satellite system definition study. Report no. 4: Management approach recommendations

A management approach for the Earth Observatory Satellite (EOS) which will meet the challenge of a constrained cost environment is presented. Areas of consideration are contracting techniques, test philosophy, reliability and quality assurance requirements, commonality options, and documentation and control requirements. The various functional areas which were examined for cost reduction possibilities are identified. The recommended management approach is developed to show the primary and alternative methods.

Source record↗